{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sc-fegan-face-editing-generative-adversarial","title":"SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color","arxiv_id":"1902.06838","date":"2019-02-18","proceeding":"ICCV 2019 10","authors":["Youngjoo Jo","Jongyoul Park"],"abstract":"We present a novel image editing system that generates images as the user\nprovides free-form mask, sketch and color as an input. Our system consist of a\nend-to-end trainable convolutional network. Contrary to the existing methods,\nour system wholly utilizes free-form user input with color and shape. This\nallows the system to respond to the user's sketch and color input, using it as\na guideline to generate an image. In our particular work, we trained network\nwith additional style loss which made it possible to generate realistic\nresults, despite large portions of the image being removed. Our proposed\nnetwork architecture SC-FEGAN is well suited to generate high quality synthetic\nimage using intuitive user inputs.","url_abs":"http://arxiv.org/abs/1902.06838v1","url_pdf":"http://arxiv.org/pdf/1902.06838v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sc-fegan-face-editing-generative-adversarial","repo_url":"https://github.com/GeorgeLekala/ML","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"sc-fegan-face-editing-generative-adversarial","repo_url":"https://github.com/KumapowerLIU/DeFLOCNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"sc-fegan-face-editing-generative-adversarial","repo_url":"https://github.com/SilentFalls/tog","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"sc-fegan-face-editing-generative-adversarial","repo_url":"https://github.com/run-youngjoo/SC-FEGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"facial-inpainting","task_name":"Facial Inpainting"},{"task_slug":"form","task_name":"Form"},{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"image-inpainting","task_name":"Image Inpainting"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"gated-convolution","method_name":"Gated Convolution"},{"method_slug":"glu","method_name":"Gated Linear Unit"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"patchgan","method_name":"PatchGAN"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"},{"method_slug":"wgan-gp-loss","method_name":"WGAN-GP Loss"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.06838","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}